97 results about "Event graph" patented technology

A method, system and computer readable article of manufacture for displaying visualization from an event log. According to the method, sequences are extracted from the event log. The event log is a record of a plurality of events that occurred. A sequence graph is generated from the sequences. The sequence graph includes nodes representing the events of the event log and the events of each node have a common property. A sequence graph visualization is displayed from the sequence graph. The sequence graph visualization includes (i) node icons representing the nodes of the sequence graph, (ii) node transition icons representing relationships between nodes, and (iii) an event icon for each of the one or more events of a corresponding node.

The invention discloses an event atlas construction system and method based on multi-dimensional feature fusion and dependency syntax. The event graph construction method based on multi-dimensional feature fusion and dependency syntax is realized through joint learning of event extraction, event correction and alignment based on multi-dimensional feature fusion, relationship extraction based on enhanced structured events, causal relationship extraction based on dependency syntax and graph attention network and an event graph generation module. According to the event graph construction method and device, the event graph is constructed through the quintuple information of the enhanced structured events and the relations between the events in four dimensions, and the defects that in the priorart, event representation is simple and depends on an NLP tool, the event relation is single, and the influence of the relations between the events on event graph construction is not considered at the same time are overcome. According to the event atlas construction method provided by the invention, the relationships among the events in four dimensions can be randomly combined according to different downstream tasks, and the structural characteristics of the event atlas are learned to be associated with potential knowledge, so that downstream application is assisted.

A method for analyzing network incidents within a network and providing prioritized remediation recommendations is disclosed. The method includes: receiving network data and computing a plurality of network incidents from the network data, collecting network incidents related to a particular network issue over a time period and grouping the network incidents according to root-cause symptoms, generating a network incident graph by superimposing groups of network incidents over a network graph, analyzing the network incident graph to identify localized areas with systemic issues; and based on the analysis of the network incident graph, generating and displaying a list of remediation recommendations. Each remediation recommendation includes a systemic issue in the network, a remediation to resolve the issue, and a quantified expected benefit from implementing the remediation.

The invention relates to a domain event graph construction method and device fusing multiple types of facts and entity knowledge. The method comprises the following steps: performing cause relationship extraction and instance cause element extraction on a domain corpus to form an instance cause logic knowledge base; constructing an abstract concept knowledge base with hierarchy; performing entityword abstraction and predicate abstraction on instance events in the instance cause logic knowledge base by utilizing the abstract concept knowledge base to form an abstract cause graph; carrying outentity linking on instance events in the instance cause logic knowledge base by utilizing the entity knowledge graph and adopting an entity linking technology, and fusing event knowledge and entity knowledge to form a cause knowledge graph; and combining the abstract factorial map with the factorial knowledge map to form a domain event map. The advantages of static entity knowledge and action event knowledge can be integrated, the application range of knowledge questions and answers can be widened, and the domain event graph can be used as a common knowledge base to expand domain language resources.

The invention relates to an information classification extraction method and device based on a classifier, computer equipment and a storage medium. The information classification extraction method comprises the steps of obtaining to-be-classified information; performing word segmentation processing on the to-be-classified information to obtain a plurality of text blocks; inputting the plurality oftext blocks into a trained classifier, classifying the plurality of text blocks, and determining a target event category and a plurality of event elements of the to-be-classified information; extracting corresponding event element parameters according to the target event category and the event elements, and outputting the target event category, the plurality of event elements and the corresponding event element parameters; and generating event atlas data corresponding to the target event category according to the plurality of event elements and the corresponding event element parameters. By adopting the information classification extraction method, the event information can be accurately and effectively classified, and the key event information can be extracted.

A pattern extraction apparatus for extracting a pattern of event occurrence from event time-series data generates an adjacent event graph by fetching adjacent events from the event time-series data, representing each of the adjacent events as a node, connecting the nodes by a directed link having a transition direction between the adjacent events and a weight, representing identical events as a single node, and, if there are a plurality of directed links between identical adjacent events, accumulating weights of the directed links into a single directed link. The pattern extraction apparatus cuts a directed link having an evaluation value smaller than or equal to a predetermined value in the generated adjacent event graph, the expected value being obtained based on the weight of the directed link.

The invention discloses a high-dynamic target detection method based on an event camera, and the method comprises the steps: obtaining event flow data from the event camera, and converting an event flow into an event image; adjusting the size of the event image, and converting the event image into a uniform resolution; putting the event image into a convolutional neural network to obtain a plurality of prediction boxes; and obtaining a detection result with relatively high confidence through non-maximum suppression. According to the invention, the event camera and the deep learning are combined, so that the capability of resisting illumination interference and complex background environment interference of high-dynamic target detection is improved.

The invention provides an event map construction method and device, and electronic equipment, and relates to the technical field of information mining, and the method comprises the steps of obtainingmultiple target event texts of a target event; performing meta-event extraction on the target event text to obtain a plurality of original meta-events; wherein the original meta event comprises a first event keyword; determining a plurality of target meta-events matched with the fact map from all the original meta-events based on the preset fact map and the first event keyword; wherein the cause map comprises events connected according to an event causal relationship and / or an event time sequence relationship; and updating the events in the cause map according to the target meta-event to obtain an event map corresponding to the target event text. According to the invention, the complexity of generating the event graph can be reduced, and the obtaining efficiency and event tracking capability of the event graph are improved.

The invention discloses a dam safety operation-oriented event atlas construction method. The method comprises the following steps of: (1) embedding enhanced semantic information by using word vectors; (2) introducing local attention and global attention to capture keywords and mine document information; (3) solving a sample imbalance problem by using Focalloss and extracting an event type; (4) adding event type codes, connecting the event type codes in series to form a new sentence code, and processing embedded vectors after series connection by using BiLSTM; (5) converting the attention network and the attention network according to the proportion, extracting effective features, and capturing corresponding event arguments; (6) filling an event missing document level argument by using an argument filling network; and (7) converting the attention network and the attention network labeling sequence by the weighted fusion graph, obtaining a causal relationship between events, and constructing an event graph.

The invention discloses a method for predicting group events through an event knowledge graph, and relates to the technology of event knowledge graph mining and application. The method is implementedthrough the following technical scheme: the method comprises two stages of prediction model training and real-time prediction; in the prediction model training stage, historical structured event dataare used as input data to construct a historical event graph, a graph embedding network is adopted to learn vectorized representation of the event graph, and then whether an event occurs or not is predicted based on a classification network model established by a deep neural network. In the real-time prediction stage, real-time structured event data are used as input data to construct a real-timeevent graph, the real-time event graph is used as input data of a trained event prediction model, the event graph constructed by the real-time event data is input into the event prediction model, vectorized representation of the event graph is obtained, and then deep semantic information of the event data is mined and is converted into a dichotomy problem that an event occurs or does not occur, and a result with the maximum probability is taken as a prediction result of whether the event occurs or not.

A method for analyzing network incidents within a network and providing prioritized remediation recommendations is disclosed. The method includes: receiving network data and computing a plurality of network incidents from the network data, collecting network incidents related to a particular network issue over a time period and grouping the network incidents according to root-cause symptoms, generating a network incident graph by superimposing groups of network incidents over a network graph, analyzing the network incident graph to identify localized areas with systemic issues; and based on the analysis of the network incident graph, generating and displaying a list of remediation recommendations. Each remediation recommendation includes a systemic issue in the network, a remediation to resolve the issue, and a quantified expected benefit from implementing the remediation.

When the balance of an amount of savings, which can be used when designing a lifestyle by a user, is calculated, an user interface which is as easy to operate as possible can be provided. On a display screen operated by the user, an event icon which is an element for at least one computation, and an event set-up section for specifying at least a one-dimensional value as a result of the event icon being set up by an operation are displayed. When the event icon is dragged-and-dropped by an operation to the event set-up section, a predetermined computation is performed on the basis of the position thereof, and on the basis of the computation result, the amount of savings is displayed.

The invention provides an event atlas-based journey prediction method, a system, a device, and a storage medium. The method comprises the steps: generating event nodes and directed edges according to network travel text information, and obtaining a travel event atlas; inputting the node sequence with the weight into a network modeling tool, and constructing a weighted directed graph; inputting the weighted directed graph into a graph neural network model, and generating vector representation of each node in the tourism event graph; constructing a sub-graph related to each event chain according to the weighted and directed graph, and obtaining a corresponding sub-graph adjacency matrix A; inputting the samples into the graph neural network model in batches to obtain vector representation of nodes; inputting the sub-graph adjacency matrix A and the vector representation into a gated graph neural network, and outputting a sub-graph event representation; and determining a candidate event according to the candidate event node with the highest correlation score. According to the method, information extraction and fusion can be carried out on the tourism field data, the tourism event map is constructed, accurate journey prediction is carried out, and the labor cost is greatly saved.

Provided are systems and methods for generating an event map of events located near a user's location. The event map may include embedded media, such as audio, video, images, and the like for events located within a specific geographic area, such as geographic radius around a user's location, and a specific timeframe. Each event may be displayed at a position on the event map that corresponds to the location of the event, and the embedded media for the event may be displayed at an event's position. Additionally, the event map may include data for each event, such as date and time of the event and links to event-related resources.

The invention provides an event portrait-oriented text analysis method for a dialogue system. The event portrait-oriented text analysis method comprises the following steps of: 1, extracting an event relationship; 2, event extraction and structuring; 3, event similarity calculation and fusion; 4, event trigger word extraction and argument extraction. According to the method, the method for constructing the event graph is fully researched, the logic structure relation between events is described by adopting a knowledge graph structure, and meanwhile, in order to describe the events more clearly, the multi-dimensional attributes of the events are portrayed. The invention provides a system structure constructed by a affair graph. The structure comprises text corpus collection, event relation extraction template construction, event extraction and structuring, event similarity calculation and fusion, event trigger word extraction and argument extraction model construction, event relation pair construction and graph database storage. In addition, by utilizing the system structure, a sequential affair map, a causal affair map, a conditional affair map, a turning affair map and a concurrent affair map are constructed.

The invention relates to the technical field of data processing, is applied to the financial industry, and particularly relates to a data search matching method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a keyword to be searched and an event graph corresponding to the keyword; according to the relationship among the nodes in the event graph, calculating the distance between the node to which the keyword belongs in the event graph and other nodes in the event graph, and extracting the nodes of which the distances are smaller than a first preset threshold value to obtain a first expansion keyword; according to the relationship among the nodes in the target encyclopedia atlas, calculating the distance between the node to which thekeyword belongs in the target encyclopedia atlas and other nodes in the target encyclopedia atlas, and extracting the nodes of which the distances are smaller than a second preset threshold value to obtain a second expansion keyword; and combining the keyword, the first extended keyword and the second extended keyword to obtain a keyword set, and performing search based on the keyword set and a preset search and analysis engine to obtain a search result.

The invention relates to an abnormity detection method based on a log event graph and association relationship mining. The method comprises the following steps: collecting an original log of a system to obtain log events; segmenting the log events into different groups according to a set time span or a task number, wherein the log events in each group form a log event sequence according to the generated time; according to association relationship mining, mining system log events having association relationships with each abnormity, and removing log events irrelevant to the abnormities in the log event sequence; extracting a semantic vector of each log event as a feature vector of the log event; generating a bidirectional full-connection log event graph according to the log event sequence, updating a feature vector of each node by using a gating graph neural network, performing weighted summation on the updated feature vectors of all the nodes by using an attention network, calculating a global feature vector of the log event graph, finally performing classification detection through a full-connection network, and obtaining the normal or abnormal type of the system.

An automated technique for security monitoring leverages a labeled semi-directed temporal graph derived from system-generated events. The temporal graph is mined to derive process-centric subgraphs, with each subgraph consisting of events related to a process. The subgraphs are then processed to identify atomic operations shared by the processes, wherein an atomic operation comprises a sequence of system-generated events that provide an objective context of interest. The temporal graph is then reconstructed by substituting the identified atomic operations derived from the subgraphs for the edges in the original temporal graph, thereby generating a reconstructed temporal graph. Using graph embedding, the reconstructed graph is converted into a representation suitable for further machine learning, e.g., using a deep neural network. The network is then trained to learn the intention underlying the temporal graph. The approach operates to understand running behavior of programs, to classify them, and then enable detection of potential malicious behaviors.

The invention discloses a fusion graph construction method and device. The method comprises the steps of obtaining event texts from information sources; for any event text, determining an event group with a causal relationship in the event text through a machine learning algorithm, and determining causal strength between a reason event and a result event in the event group; determining an event atlas according to each event group and the causal strength between the reason event and the result event in each event group; and constructing a graph relationship through each event group in the event graph and an entity in the knowledge graph, thereby forming a fusion graph. According to the mode, city work is evaluated by using the knowledge graph fused with the event graph, dependence on expert experience can be avoided, and the city situation can be monitored or evaluated only according to the incidence relation between the events occurring in the city operation process and the knowledge graph.

The invention discloses a time sequence point-based network attack event prediction method, system and device and a medium, and the method comprises the steps: obtaining historical attack event data, generating a first event sequence according to the historical attack event data, and determining a first event feature and event occurrence time; constructing a first event atlas according to the first event feature, and processing the first event atlas through a graph embedding algorithm to obtain a first feature vector; inputting the first feature vector and the event occurrence time into a pre-constructed recurrent neural network for model training of a depth point process to obtain a trained time sequence point process model; and predicting the occurrence time and the event type of the next network attack event according to the time sequence point process model. According to the method, the event atlas is converted into the feature vectors through graph embedding, the feature vectors and the occurrence time are trained through the depth point process, the accuracy of network attack event prediction is improved, and the method can be widely applied to the technical field of network security.

The embodiment of the invention discloses an information recommendation method and device, and relates to the field of artificial intelligence. According to the specific implementation scheme, the method comprises the steps: enabling a server to use sample information to train a classification model in advance, employing the classification model for mining affiliation between events in an event database to obtain an event graph, and storing the affiliation, wherein nodes in the event graph are the events, and if edges exist between the events, it shows that the two events are affiliation; after a recommendation request sent by a user through the terminal equipment is received each time, determining a first event in which the user is interested according to historical browsing records and the like of the user, determining a second event which is a subordinate event with the first event on the basis of the first event and the event map, and sending target information containing the second event to the terminal equipment. By the adoption of the scheme, whether the two events are subordinate events or not is determined through the classification model trained through the characteristics of the events, the purpose of mining the subordinate relationship between the events is achieved, and then accurate recommendation is achieved.

The invention provides a movie script plot prediction method. The method comprises the following steps: segmenting a historical text into sentences; determining an event contained in the statement andan event feature of the event; establishing an event graph according to the time sequence of each event; wherein each node in the event graph corresponds to one event, and the feature vector of the node is determined through the event feature of the event corresponding to the node; and inputting the feature vectors of the nodes contained in the specified node path into the trained graph neural network, and outputting the prediction node of the event corresponding to the specified node path. By adding the event characteristics of the event, the event information is enriched, so that the accuracy of movie theatre plot prediction is improved.

The invention provides an information event mapping system and method based on natural language processing. The system comprises an information classification screening module, a part-of-speech tagging and grammar analysis module, a text syntax tree construction module, a subject-called-object analysis and extraction module, a multi-sentence entity anaphora resolution module and a knowledge graphdisplay module. By implementing the method and the system, analysts can be helped to know key information of the information more visually, so that the information reading work efficiency and the analysis accuracy of the analysts are improved.

The invention discloses an early warning event graph construction method, and relates to the field of big data, and the method comprises the steps: obtaining text information for event graph construction when an event graph construction instruction is received; performing event acquisition on the text information to obtain at least one piece of first event information corresponding to the text information and an early warning label corresponding to the at least one piece of first event information; screening the at least one piece of first event information to obtain at least one piece of second event information for event atlas construction; determining a hierarchical relationship between the at least one piece of second event information according to the feature information contained inthe at least one piece of second event; and storing the at least one piece of second event information in a corresponding graph database according to the hierarchical relationship so as to complete construction of an event graph. The invention further provides a device, computer equipment and a storage medium. When the event atlas is constructed, the quality of the event atlas is improved.

The invention relates to a power grid accident information automatic filling and auxiliary decision-making method based on an event graph. The method comprises the following steps: firstly, performing natural language analysis on a text of a power grid accident record, performing information extraction, obtaining a relationship between accidents and a processing condition of the same type of accidents, combining an OMS, realizing automatic classification and input of accident information according to classification labels such as an accident occurrence area range and an accident type, then constructing an ontology by using an OWL language, inputting the extracted information, constructing an event ontology, communicating the events by taking the transition probability between the events as a connection, and establishing an event graph model. According to the invention, based on the theory and method of the event graph, on the basis of a power grid accident record text, rapid, accurate and comprehensive power grid fault information extraction and automatic filling are realized through a natural language analysis technology, an instantiated power grid accident record event graph is constructed, and various functions of information search, auxiliary decision making, accident analysis and the like can be provided for operators.

The technology disclosed includes a system to reduce clutter during graph presentation for security incident analysis. The system includes logic to score nodes potentially collapsed by equivalence, of indicated interest for security incident analysis, to prevent aggregation. The system includes logic to aggregate and hide equivalent nodes that have matching degrees, that are connected to matching nodes by matching edge types, and that have scores below a first selected threshold. The system does not collapse nodes that are interesting for security analysis and keeps them visible. The technology disclosed identifies chains of at least three nodes having degrees of 1 or 2, without branching from any node in the chain. The identified chains are collapsed into chain-collapsed single nodes. Two different cases of chains including whisker chains ending in a leaf node and chains connected at both ends to two other nodes are presented.

The invention relates to the technical field of Web services, in particular to a semantic matching algorithm of a large-scale publish-subscribe system, which comprises the following steps: 1) an index is established for a subscription graph; 2) events are converted to a specific data structure; 3) the data structure of an event graph is submitted to an event matcher; and 4) the event matcher carries out the matching calculation of the event graph and the subscription graph mode, thus finding out the matching subscription and distributing the events to the matching subscribers by the system. The semantic matching algorithm can reduce the matching time of the events and the subscription, improve the matching efficiency of the events, allow the matching efficiency of the events not to be seriously affected along with the increase of the number of the subscription and publishing events and allow the performances of the system not to deteriorate. At the same time, the semantic matching algorithm fully utilizes the multi-core technology to improve the matching efficiency according to the self-adaptation adjustment matching algorithm of a core system structure of a matching server.

The invention discloses an event mode frequent subgraph mining and prediction method, relates to the technical field of knowledge engineering, and aims to reduce mining overhead and improve mining speed. According to the technical scheme, the method comprises the following steps: dividing nodes in a density graph abstract graph into clusters or super nodes in a density-based graph abstract stage,and sequentially selecting the nodes to construct a concise high-level graph; in the mode mining stage, carrying out frequent sub-graph mining on a large-scale event graph, mining frequent sub-graphsbased on the event mode, and finding frequently appearing sub-graphs in a graph set of the event graph; abstracting the input graph G based on a graph abstract algorithm, and performing frequent sub-graph mining and graph abstract-based mining preprocessing by taking an abstract result as input; and finally, mining and predicting an event mode from multiple sources and multiple perspectives by utilizing multi-source data, and performing candidate set filtering and frequent sub-graph output according to a minimum support degree min_sup defined by a user or other output standards.

The invention relates to a Chinese structured event extraction method, and belongs to the technical field of information extraction. The Chinese structured event extraction method comprises the following steps of: performing syntactic dependency structure analysis on an unstructured text statement by using a syntactic dependency analysis tool to obtain a syntactic dependency analysis tree; analyzing the characteristics of the Chinese linguistic and syntactic dependency analysis tree, constructing a core verb chain, and identifying all events existing in the statement; adding a semantic dependency relationship to the syntactic dependency analysis tree by means of a semantic dependency analysis tool, and constructing a syntactic semantic dependency analysis tree; adjusting dependency structures of event core verbs, prepositions and passive language states in the syntactic semantic dependency analysis tree, and constructing a syntactic semantic dependency analysis event graph. According to the method, data does not need to be manually annotated, the structured event can be well extracted, and the extraction accuracy and recall rate are high.